如何用Keras建立多类卷积神经网络

我正在尝试使用Keras和Tensorflow后端实现一个U-Net,用于图像分割任务。我将大小为(128,96)的图像与大小为(12288,6)的掩码图像一起输入网络,因为它们是扁平的。我有6个不同的类(0-5),它们给出了遮罩图像形状的第二部分。它们已使用to\u category()函数编码为一个热标签。目前,我只使用一个输入图像,也使用同一个作为验证和测试数据。

我希望U-Net执行图像分割,其中类0对应于背景。当我现在只训练U-Net几个时代(1-10)时,得到的预测掩码图像似乎只是给每个像素随机分类。当我对网络进行更长时间的训练(50多个历元)时,所有像素都被分类为背景。由于我使用相同的图像进行训练和测试,我发现这很奇怪,因为我正在加速网络训练。我如何解决这个问题?我给网络提供掩码图像和真实图像的方式是否有问题?

我曾尝试手动给网络赋予权重,以减少对背景的强调,与其他类相比,我还尝试了不同的损失组合、不同的掩模图像塑造方法和许多其他方法,但都没有取得好的效果。

this repository . 我设法把它训练成两个班的情况,取得了很好的效果,但我现在不知道如何将它扩展到更多的班。

def get_unet(self):

    inputs = Input((128, 96,1))
    #Input shape=(?,128,96,1)

    conv1 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',
      kernel_initializer = 'he_normal', input_shape=(None,128,96,6))(inputs)
    #Conv1 shape=(?,128,96,64)
    conv1 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',
          kernel_initializer = 'he_normal')(conv1)
    #Conv1 shape=(?,128,96,64)
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
    #pool1 shape=(?,64,48,64)


    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same',
         kernel_initializer = 'he_normal')(pool1)
    #Conv2 shape=(?,64,48,128)
    conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same',
         kernel_initializer = 'he_normal')(conv2)
    #Conv2 shape=(?,64,48,128)
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
    #Pool2 shape=(?,32,24,128)

    conv5 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',
         kernel_initializer = 'he_normal')(pool2)
    conv5 = Conv2D(256, (3,3), activation = 'relu', padding = 'same',
         kernel_initializer = 'he_normal')(conv5)

    up8 = Conv2D(128, 2, activation = 'relu', padding = 'same',
        kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv5))
    merge8 = concatenate([conv2,up8], axis = 3)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same',
         kernel_initializer = 'he_normal')(merge8)
    conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same',
         kernel_initializer = 'he_normal')(conv8)


    up9 = Conv2D(64, (2,2), activation = 'relu', padding = 'same',
        kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
    merge9 = concatenate([conv1,up9], axis = 3)
    conv9 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',
        kernel_initializer = 'he_normal')(merge9)
    conv9 = Conv2D(64, (3,3), activation = 'relu', padding = 'same',
        kernel_initializer = 'he_normal')(conv9)
    conv9 = Conv2D(6, (3,3), activation = 'relu', padding = 'same',
        kernel_initializer = 'he_normal')(conv9)

    conv10 = Conv2D(6, (1,1), activation = 'sigmoid')(conv9)
    conv10 = Reshape((128*96,6))(conv10)

    model = Model(input = inputs, output = conv10)
    model.compile(optimizer = Adam(lr = 1e-5), loss = 'binary_crossentropy',
          metrics = ['accuracy'])

    return model